Following are descriptions of our most significant work related to this project over the past year. Paragraphs 1-3 cover work that falls within the phenomics area described in our goals and objectives. Paragraphs 2 and 4 address heterogeneity reduction and subgrouping themes also described in the goals and objectives. Paragraphs 5-7 address other related research activities in connection with this project. (1) Earlier work by the group summarized the Branch's cognitive data using six cognitive domain composite variables (for verbal memory, visual memory, n-back working memory, processing speed, card sorting, and span working memory) and a higher order factor reflecting general cognitive ability, also called g. A recent application of these composite scores has been to explore genome-wide genetic associations in the CBDB samples. We identified an exciting and novel association between our general cognitive composite, g, and a genetic variant related to sodium channel biology that helps to explain the cognitive impairment in our sample of people with schizophrenia and in their unaffected siblings. This association would not have been detected without the cognitive data aggregation strategy and composite scores developed by the Neuropsychology group. The association is also supported by analyses of genotype effects in fMRI data and analyses of gene transcript expression in post-mortem brain tissue. These findings have been presented at genetics and psychiatry conferences and a comprehensive manuscript is under review at JAMA Psychiatry. (2) The group's work extends beyond cognition to other sorts of behavioral data. Wallwork et al. (2012) described our efforts to validate a five-dimension structure that better reflects psychotic symptom data from the Positive and Negative Syndrome Scale (PANSS) than the three dimensions originally proposed for that scale. We tested variations of this model using statistical modeling in the CBDB schizophrenia data and in an independent data set from Japanese collaborators. These analyses supported construction of new PANSS composite scores for positive, negative, agitated, concrete/disorganized symptoms, and general distress. These composites are now in use in CBDB neuroimaging and genetics studies. One set of current analyses using these variables is examining (e.g., through latent class analysis) how the PANSS composites may be helpful in identifying illness subgroups (in particular, a high negative symptom/low distress or deficit syndrome subgroup). Other current work is examining how PANSS data relates to cognitive and brain structure data in our schizophrenia sample and in the unaffected siblings of these cases (some of the siblings show sub-clinical levels of symptomatology). (3) Analogous lines of work are examining the dimensions that underlie typical and abnormal personality in the CBDB data. Leading theories of personality posit five dimensions (neuroticism, extraversion, openness to experience, conscientiousness, and agreeableness). Using this model as a starting point, we have elaborated and are refining new dimensional models for the Tri-dimensional Personality Questionnaire (TPQ) and for the SCID-II Personality Questionnaire (SCID-II). The TPQ targets personality in the non-clinical range. The SCID-II is used to assess disordered personality symptomatology. All of this work has been presented at scientific conferences and a paper on the SCID-II analyses is under review at Journal of Personality Disorders. (4) As noted in our goals and objectives, illness heterogeneity is a major challenge in schizophrenia research. Many kinds of studies are handicapped by a focus on broad, undifferentiated diagnosis. Using different strategies to identify robust, more homogeneous behavioral/clinical subgroups offers one means for disentangling illness heterogeneity. The large, comprehensively assessed Branch samples are well-suited for such analyses and the phenomics work already described helps create a foundation for subgrouping analyses. In Cole et al. (2012), we derived developmental trajectory subtypes based on academic and social adjustment during childhood and adolescence. With more detailed developmental data and a larger schizophrenia sample, we are conducting analyses to refine and extend the earlier analyses. Other current work addressing illness heterogeneity is seeking to extend these subgrouping strategies, simultaneously employing data from complementary data-streams (e.g., pre-onset development with post-onset symptoms and treatment response). The ultimate goal is to use robust subgrouping schemes to find genetic, brain structure, and other biological associations within or between subgroups that cannot be detected in analyses done at the level of broad diagnosis. (5) To gauge better which cognitive variables are best suited for genetics analysis, we have taken different approaches to estimate the degree to which different variables are heritable - that is, under genetic control. Correlations in cognitive performance within families that include siblings that are both affected and unaffected by schizophrenia offer one traditional way of estimating heritability. Recently, a novel technique (GCTA) has been developed that permits estimates of heritability based on whole-genome data from samples of unrelated people. We presented our first results contrasting traditional correlation and new GCTA methods at the 2012 ACNP conference and are beginning work on an associated manuscript. (6) We have recently completed an update of earlier quantitative reviews of cognitive impairment in schizophrenia, showing that the cognitive impairment seen in people with schizophrenia has been consistent in magnitude and pattern over the past 30 years, and across different geographic regions around the world (i.e., North America, Europe and Asia). This work has been presented at conferences, published as a chapter in an edited collection (Dickinson et al., 2013), and as a journal article (Schaefer et al., 2013). (7) We are also involved in collaborations with investigators outside NIH. Recent collaborations include work with investigators at the Litwin-Zucker Research Center (including former CBDB scientist Terry Goldberg) on a study of the relationship of an Alzheimers disease genetic marker with cognitive performance and brain metabolites in normal elderly individuals (Gomar et al., in press), and work with Japanese collaborators (including former CBDB post-doctoral fellow Ryota Hashimoto) on a genetic study of cognitive decline in schizophrenia (Hashimoto et al., 2013). We also collaborated with Dr. Hashimoto and Daniel Rujescu (Ludwig University in Germany) in connection with the cognition/sodium channel biology association work described in paragraph 1, above.